# Signal Discovery

> Signal discovery is the search for variables that predict returns. Learn where ideas come from, how to test signals with information coefficients and decay curves.

Source: https://learn.tradelabsai.com/research/signal-discovery/  
Track: Research and Backtesting · Level: Advanced · Updated: 2026-10-03  
Publisher: TradeLabs AI (https://tradelabsai.com). Education, not financial advice.  
Cite as: TradeLabs Learn, "Signal Discovery", https://learn.tradelabsai.com/research/signal-discovery/

A trading signal is any piece of information that helps predict future returns, volatility or other outcomes: a price pattern, a valuation ratio, an analyst revision, a change in order flow or a shift in sentiment. Signal discovery is the process of generating ideas, turning them into measurable signals and testing whether they predict anything useful. Good signal research combines creativity with strict statistical discipline, because most candidate signals are noise.

## Where signal ideas come from

| Source | Examples |
|---|---|
| Economic reasoning | Cheap stocks should earn more if they are riskier or neglected. See [Value Factor](https://learn.tradelabsai.com/research/value-factor/) |
| Behavioural finance | Investors underreact to news or overreact to trends |
| Market structure | Index rebalancing, forced selling, liquidity provision. See [Index Rebalancing](https://learn.tradelabsai.com/fundamentals/index-rebalancing/) |
| Academic research | Published anomalies, with caution about decay. See [Reading Academic Papers](https://learn.tradelabsai.com/start-here/reading-academic-papers/) |
| Alternative data | Card spending, web traffic, satellite images. See [Alternative Data Explained](https://learn.tradelabsai.com/alternative-data/alternative-data-explained/) |
| Observation and experience | Patterns noticed while trading, then tested |

Signals with a clear reason to work are much more likely to survive out of sample.

## Turning an idea into a signal

1. **Define precisely:** for example, "change in consensus EPS estimate over the past month divided by price".
2. **Ensure point in time availability.** See [Point-in-Time and Survivorship-Free Data](https://learn.tradelabsai.com/programming/point-in-time-data/).
3. **Standardise:** rank or z score across assets on each date. See [Percentiles, Quantiles and Z-Scores](https://learn.tradelabsai.com/math/z-scores/).
4. **Handle outliers:** winsorise or rank. See [Outliers and Robust Statistics](https://learn.tradelabsai.com/math/outliers-and-robust-statistics/).

## Measuring predictive power

### Information coefficient (IC)

The IC is the correlation between signal values and subsequent returns across assets on each date, often using rank correlation.

```
IC_t = rank correlation(signal_t, return_(t+1))
```

| IC (cross sectional, monthly) | Interpretation |
|---|---|
| Around 0.02 to 0.05 | Typical for useful equity signals |
| Above 0.1 | Very strong; check for errors or leakage |
| Near 0 | No predictive power |

Even small ICs can be valuable when applied across many assets. Richard Grinold's fundamental law of active management links skill and breadth:

```
information ratio ≈ IC × √(breadth)
```

where breadth is the number of independent bets per year.

### Quantile analysis

**Example: Sorting stocks into quintiles**
A researcher ranks 1,000 stocks each month by a revisions signal and splits them into five groups. Average next month returns: top quintile +1.2%, bottom quintile +0.4%, with returns rising steadily across groups. The top minus bottom spread is 0.8% a month. A monotonic pattern across quintiles is more convincing than a result driven by one extreme group. The researcher then checks the spread after costs, in different periods and with sector neutral ranking.

## Decay and horizon

Signals predict best over certain horizons. Plotting IC against holding period shows how quickly information is absorbed: short term signals may decay within days, while value signals persist for months. Matching rebalancing frequency to signal decay keeps costs under control. See [Signal Turnover, Breadth and Neutralization](https://learn.tradelabsai.com/research/signal-turnover/) and [Signal and Alpha Decay](https://learn.tradelabsai.com/research/signal-and-alpha-decay/).

## Avoiding false discoveries

- **Record every signal tested.** See [P-Hacking and Multiple Testing](https://learn.tradelabsai.com/research/p-hacking-and-multiple-testing/).
- **Use out of sample periods.** See [In-Sample vs Out-of-Sample Testing](https://learn.tradelabsai.com/research/out-of-sample-testing/).
- **Control for known factors:** a new signal may just repackage size, value or momentum. See [Factor Models](https://learn.tradelabsai.com/portfolio/factor-models/).
- **Check robustness** across universes and definitions.
- **Watch for leakage.** See [Data Leakage](https://learn.tradelabsai.com/research/data-leakage/).

## Frequently asked questions

### What is a trading signal?

A measurable piece of information, such as a valuation ratio, price pattern or estimate revision, that helps predict future returns or risk.

### What is an information coefficient?

The correlation between a signal's values and subsequent returns across assets, a common measure of a signal's predictive power.

### How do I know if a new signal is real?

It should have a sensible rationale, a consistent quantile pattern, positive out of sample results, survive costs and add value beyond known factors.

Next, learn why signals lose power over time in [Signal and Alpha Decay](https://learn.tradelabsai.com/research/signal-and-alpha-decay/).

## Continue learning

- Next lesson: [Signal and Alpha Decay](https://learn.tradelabsai.com/research/signal-and-alpha-decay/)
- Previous lesson: [What Is Alpha?](https://learn.tradelabsai.com/research/what-is-alpha/)
- Related: [What Is Alpha?](https://learn.tradelabsai.com/research/what-is-alpha/): Alpha is return beyond what market and factor exposure explain. Learn how alpha is measured, the difference between alpha and beta, and why true alpha is rare.
- Related: [Combining Signals](https://learn.tradelabsai.com/research/combining-signals/): Combining several weak signals often beats relying on one strong one. Learn standardisation, weighting methods, correlation between signals and pitfalls to avoid.
- Related: [Signal and Alpha Decay](https://learn.tradelabsai.com/research/signal-and-alpha-decay/): Signal decay is how fast a signal's predictive power fades; alpha decay is how edges shrink over years. Learn both, the evidence and how traders adapt.
- Related: [Feature Engineering](https://learn.tradelabsai.com/machine-learning/feature-engineering/): Features are the inputs that give trading models a chance. Learn the main feature families, how to make them stationary and comparable, and how to avoid leakage.
- Related: [P-Hacking and Multiple Testing](https://learn.tradelabsai.com/research/p-hacking-and-multiple-testing/): Testing many strategy variations guarantees some look good by chance. Learn how p hacking happens, how to adjust for multiple tests and the deflated Sharpe ratio.